Life as a pedestrian, cyclist, or scootist in the Washington region can be a harrowing experience. Vehicles blocking crosswalks or standing in bike lanes are commonplace occurrences that put everyone at risk—especially those of us not protected by two tons of steel.

While these experiences can be frustrating, even rage inducing, there is a tool you can use for a bit of catharsis: @HowsMyDrivingDC. Tweet any license plate at this seven-month old Twitter bot, and it will return all the vehicle’s outstanding parking and speed camera citations in DC.

Daniel Schep, a software engineer and Transportation Techies hack night MVP, developed @HowsMyDrivingDC when he observed some in the #bikeDC community were taking photos of dangerous driving behavior, then manually looking up the plate on the DC DMV website and tweeting the violation and outstanding citations side-by-side.

on a whim, I decided to see whether the car that was blocking the Noma bike lane this morning had any outstanding parking tickets…turns out this car owes the city nearly $2000! just in the last 12 months!!#VisionZero#BikeDCpic.twitter.com/oFWksG653u

Schep pursued an automated solution, just to see if it could be done, and a week later, @HowsMyDrivingDC was born!

Which state’s drivers are most dangerous?

According to Schep, whenever @HowsMyDrivingDC comes up in conversation, the first question is always: which state has the most tweets and citations? Now that the bot is seven months old, Schep provided me with recent underlying data, so I could try to answer that question.

As of 2/23/2019

DC

MD

VA

Total

Tweets

702

691

433

2,285

Tweets w/ Citations

308

280

161

807

Total Citation Value

$221K

$417K

$151K

$861K

Avg. Citation Value per Tweet

$583

$1,378

$1,001

$959

% Citations at least 60 days old

31%

35%

35%

34%

% Citations at least 365 days old

6%

19%

3%

10%

As shown in the table above, Maryland vehicles represent over half of the overall citation value logged by the app, and are virtually tied with DC vehicles for total tweets. Since there is no reciprocity between DC, Maryland, and Virginia DMVs, there is much less incentive for Maryland and Virginia drivers to pay their outstanding citations.

The lack of incentive for out-of-state drivers to pay citations is highlighted further when looking at the age of citations. Regardless of state, approximately a third of all citations not being adjudicated are at least 60 days old. However, one in five citations issued to Maryland vehicles is at least a year old, while DC and Virginia vehicles have a low percentage, 6% and 3%, respectively, for citations of the same vintage.

This trend in the Twitter bot data is consistent with a report out today from WTOP showing that of the $324 million in citations issued in DC in FY2018, $139 million remain unpaid. Maryland vehicles account for 43% of these unpaid tickets.

An app is born

After conducting a similar analysis last fall, Schep and I decided to develop a mobile app version of @HowsMyDrivingDC and enter into the DC Office of Chief Technology Officer GigabitDCx competition for a chance to win funding. We were selected as one of six finalists and dove headfirst into developing the How’s My Driving app.

The primary purpose of the app is to harness the collective power of pedestrians, cyclists, and scootists to capture the dangerous driving behavior that affects them the most. Our ultimate goal is to leverage the app for targeted, real-time enforcement to get the most dangerous drivers off the road.

Here’s how the app works right now:

Let’s say you come across this vehicle blocking a crosswalk in Barracks Row….

Once your violation is submitted, a summary page shows that this vehicle has six outstanding citations totalling over $1,000. Since at least two tickets are over 60 days past due, the vehicle is eligible for tow. This vehicle’s most frequent violations are for speeding, indicating a pattern of dangerous driving.

Integrating into existing DC government infrastructure

Schep and I didn’t create this app just to give angry pedestrians and cyclists an outlet to complain about dangerous driving behavior. By connecting our app to relevant government systems, we can optimize enforcement and provide data to support new initiatives like loading zones for ride-hailing vehicles. Here are some examples:

Department of For-Hire Vehicles (DFHV): DFHV is the DC department that has jurisdiction over taxis and to a lesser extent, ride-hailing vehicles like Uber and Lyft. Currently, complaints about such vehicles can be submitted to DFHV via phone or web form. DFHV collected more than 1,000 complaints last year, and fewer than 100 of them came through the form.

Schep and I have been working with DFHV to ensure that violations reported through our app about taxis and ride-hailing vehicles are entered directly into their system. With more complaints submitted, DFHV can make the case for increased jurisdiction over ride-hailing vehicles such as mandating data sharing, implementing dynamic vehicle caps and suspending habitually dangerous drivers.

DC 311: Soon, when How’s My Driving app users submit a violation, parking enforcement requests and DC fleet vehicle complaints will automatically be reported to DC 311. This functionality layers onto our DFHV integration, meaning if a user reports a violation about an Uber, both DFHV and 311 will be notified.

Alternatively, if a user reports a violation by a MPD vehicle, the complaint will only be reported to the Office of Risk Management via 311. Fun fact—only 300 such complaints were submitted via 311 last year. We hope this app will be an easy way to hold DC fleet vehicles accountable. DC Fleet vehicles should be the example of how to behave on our city street, not the exception.

Our long-term aspirations

While integrating into existing DC enforcement infrastructure is great in the short term, DC’s enforcement mechanisms need a major overhaul in order to affect real change. Currently, DPW’s towing activity prioritizes emergency tows first, rush hour tows second, and 311 requests last. Rush hour tows will need to be deprioritized in favor of 311 requests in order to deploy the resources necessary to immobilize dangerous drivers.

Additionally, parking enforcement dispatch via 311 will need to be re-imagined. Currently, all parking enforcement requests are considered equally and require a 311 employee to manually dispatch to DPW. We imagine a system where parking enforcement requests submitted through the app are prioritized and dispatched automatically based on the vehicle’s citation history and proximity to DPW parking enforcement agent. Using machine learning to automatically read license plates, the app will be able to verify the legitimacy of the request.

The ultimate goal of the app is not to ticket and tow vehicles, but to act as a deterrent. If drivers know that anyone can snap a photo and log their dangerous behavior, they may think twice before breaking the law and putting other road users at risk.

While these ideas may seem bold and far-fetched, that is exactly what we need to reach the city’s Vision Zero goals and make DC streets a safe place for everyone. And we already have some big believers in DC government. We were recently named runner up in the GigabitDCx competition and awarded $9,000 to continue developing our app.

Excited #GigabitDCx awarded $9,000 to @HowsMyDrivingDC, supporting their work to take citizen participation with road safety beyond the Twitter bot. Their app has the potential to dramatically increase awareness of safe and unsafe uses of our streets. https://t.co/khMjKIU8TD

In early September 2018, Capital Bikeshare launched its CaBi Plus electric-assist pilot, introducing 80 black-painted bikes that give you a boost while you ride. The pilot is slated to run through the end of the year, and since we're approximately at the halfway point, now is a great time to dive into the data and see how the pilot is going.

CaBi provides public trip data each month that distinguishes CaBi Plus trips by bike number, which is how I was able to parse out the pedal and e-assist bike trends. For the purposes of this article, I refer to the traditional red CaBi bikes without the electric-assist as “CaBi Classic.”

CaBi Plus bicycles are twice as popular as the CaBi Classics

The table above shows that CaBi Plus trips remain largely consistent across the first two months of the pilot and increase slightly in October, which is the first month we have full data about the pilot.

The percentage of CaBi Plus trips is impressive for two reasons. First, CaBi Plus bikes represent only 2% of the total CaBi fleet, but 4% of trips.

Secondly, the number of CaBi Plus bikes used month-over-month actually decreases from 82 in September to 79 in October. In other words, CaBi Plus trips are overperforming by 100% in proportion to the number of bikes in the fleet, and improved on that number in October even with three less bikes.

Are they really twice as popular? There are a few ways to slice the data

DDOT has taken notice of the CaBi Plus pilot’s popularity. At the DC Bicycle Advisory Council meeting on November 7, DDOT announced that the CaBi Plus pilot would be extended indefinitely, citing the relatively high average CaBi Plus trips per bike per day compared to CaBi Classic.

Ooooooh #CaBi pilot is going to be extended “indefinitely.” The e-bikes are used 8.6x/day while the “analog” Cabi's are like 4.5x/day. ������ #bikedc

At first blush, these numbers seem reasonable and tell a simple story—CaBi Plus is twice as popular CaBi Classic. But when I went to replicate these numbers, I calculated much lower numbers: 5.5x/day and 2.6x/day respectively. How could this be?

It turns out that the DDOT-cited statistics were calculated based on the number of CaBi bikes ridden at least once per day, which does not take into account CaBi bikes that were available but not ridden on on a given day. I calculated the denominator by determining the lifespan of each bike and including it regardless of whether or not a trip was taken.

The graphs below compare my methodology (Total Fleet) with DDOT’s (Bikes Used) overlaid with the probability of precipitation, which I've found to be the the strongest predictor of ridership.

Regardless of methodology, the graphs above show that CaBi Plus is extremely popular when compared to CaBi Classic. However, the “Bike Used” methodology, shown on the right, inflates the statistic overall, while muting the impact of exceedingly high demand (i.e. start of the pilot) and low demand (i.e. bad weather). This calculation discrepancy has a greater impact on CaBi Plus, since the pilot has only 80 bikes compared to over 4,000 CaBi Classic bikes.

CaBi members are hogging the CaBi Plus bikes

Historically, CaBi members take approximately 80% of all CaBi trips, which bears out in the CaBi Classic trips during the pilot shown in the table above. CaBi Plus trips, however, are being completely dominated by members—in September, only one CaBi Plus trip was taken by a non-member!

Non-members fared slightly better in October: they were able to take 162 trips, which is still a mere 1.2% of total CaBi Plus trips.

The graphs above try to explain this member monopolization phenomenon. They compare the percent of weekday trips for CaBi Classic trips taken by members, CaBi Classic trips taken by non-members (casual), and CaBi Plus trips regardless of member type broken out by metro operating hours—peak, regular, and no metro service.

We can see that the majority of CaBi Classic trip taken by members are taken during peak metro hours, i.e. morning and evening commute. CaBi Classic trips taken by non-members are more evenly split between peak and regular metro hours (except on Friday due to “early weekend” activity).

CaBi Plus trips resemble CaBi Classic trips taken by non-members much more than by CaBi Classic trips taken by members, even though 99% of CaBi Plus trips are taken by members! With this additional data point, we can conclude that demand for CaBi Plus is so high among CaBi members that they are changing their behavior for the opportunity to ride one.

Scarcity is also likely part of the equation; there are only 80 CaBi Plus bikes spread over more than 500 CaBi stations. During the pilot period, non-members start 50% of their trips at 41 stations, while members starts 50% of their trips at 81 stations. With such a high concentration of non-member trips, it's less likely that a non-member would have the opportunity to ride a CaBi Plus bike.

CaBi Plus trips go further, longer, and faster

The table above shows that CaBi Plus trips on average take longer and go further at a higher speed than CaBi Classic trips. These figures were calculated for member trips only, since members overwhelmingly take CaBi Plus and I wanted to make the baseline for comparison as relevant as possible.

While these figure are interesting, averages often don’t tell the whole story.

CaBi Plus and CaBi Classic have similar distributions for distance and duration, though CaBi Plus' distributions are shifted more to the right. This comparison shows that only CaBi Plus bikes are used for trips between four and five miles and there are more CaBi Plus trips of 20-30 minutes.

This analysis is most interesting when we combine distance and duration to calculate speed in miles per hour. Average speeds for CaBi Classic trips max out at about 11 mph, while CaBi Plus trips max out at about 13 mph.

There is also an odd kink in the CaBi Plus curve showing very low speeds. This could represent the pilot's growing pains, such as operational challenges of keeping bikes charged.

What are you curious about?

Be on the lookout for periodic posts about CaBi Plus. I’ll be making updates to the analysis conducted here and explore additional aspects of the pilot.

Last month, DC's dockless vehicle-share pilot hit its one-year anniversary, and the participants look vastly different than they did this time last year. In 2017, all dockless providers operated using only pedal (Lime, Mobike, Ofo, and Spin) or electric-assist (Jump) bicycles. In a span of less than six months, 96% of the freestanding dockless pedal bikes (DoBi) on DC streets have disappeared and currently, the pilot is almost all electric bikes and scooters. I crunched some dockless data to try to figure out what happened to the pedal bicycles.

I used public API data from each of the original DoBi operators collected by Daniel Schep for the DC bike finder web app from mid-February to mid-September to explore the disappearance of the pedal DoBi. I’ve created a timeline of key dockless pilot events to try to explain how and why each operator left the DC market, and added these events in the following graphs for context.

Timeline of significant dockless pilot events (March-September 2018)

Image by the author.

The most notable events in the timeline above are the two DC Department of Transportation (DDOT) dockless pilot extensions in April and August, and the flurry of exit activity at the end of July by Mobike and Ofo. The second DDOT pilot extension lasts through the end of 2018 and imposes a new rule that all bikes must be locked to a fixed structure. This timeline gives useful context for the DoBi provider data.

What caused the demise of the pedal DoBi?

I don’t think there is any single factor that caused pedal DoBis to disappear. However, the following three factors combined paint a fairly full picture:

Chinese operators: Ofo and Mobike were heavily vested in international markets, and found the opportunity cost for operating in most US markets too high due to restrictive dockless pilot rules imposed by city governments, including in DC.

Rise of the scooters: Scooters have been cited as being more popular and providing higher marginal revenue when compared to pedal DoBis, which led Lime to convert most of its fleet and Spin to exit completely and retool for an all-scooter fleet.

DDOT pilot extensions: DoBi operators signed up for an eight-month pilot that was originally set to end in April. Assuming the pilot isn’t extended for a third time, the dockless pilot will have lasted 15 months when it ends in December. With only 400 total vehicles allowed on the street, operators felt they could not sustain a pedal bike presence for this extended period of time. The second pilot extension also outlawed freestanding DoBis, all but sealing their fate.

Here's how I sussed this out.

Mobike and Ofo both leave DC at the end of July

Ofo was the first large scale DoBi operator in the world, and Mobike was the first DoBi operator in DC. Both operators are headquartered in China, have vast international operations, and left DC market along with many other US markets within days of each other at the end of July.

These similarities aside, each operator handled its departure from DC differently. Ofo maintained about 400 bikes on the street until 7/24 — the day of their DC exit announcement. Interestingly, Ofo increases its fleet to 450 bikes several days before announcing their exit, which points to a relatively sudden decision to leave DC. Once the announcement is made, the number of Ofo bikes drops immediately from 400 to 18.

In contrast, Mobike gradually pares down its fleet over several months. On April 27, DDOT announced the extension of the dockless pilot through August, and over the following week, Mobike increases its fleet from 275 to more than 400 bikes. From this point on, Mobike allows its fleet to gradually decrease, dipping below 250 bikes as of July 20 when the Mobike app goes dark — four days before it announces its exit from the DC market.

Lime transitions from pedal bikes to electric scooters

Image by the author.

When Lime introduced dockless electric scooters on March 10, 2018, this proved to be the first chink in the armour of the pedal DoBi. The number of Lime bikes steadily declined after this point, while scooters steadily increased until they overtook bicycles in mid-May. That’s only a few days before Lime announced it would ramp up its scooter fleet, citing overwhelming scooter popularity.

The average Lime scooter introduced from March to mid-August lasts only 23 days on the street, as seen by the large swings in Lime’s scooter fleet in June and July.

Lime tried to combat this issue by exceeding the pilot-mandated 400-vehicle cap in August, but there is still a significant drop in the fleet over the the first two weeks of September. Over this time period, Lime used at least 1,885 scooters — replacing the scooter fleet 4.5 times!!

Please note that from March to June, the number of bikes and scooters are the street are largely estimates. Lime did not differentiate between the two vehicle types in its API until June, so I applied a proportion of known bikes and scooters to estimate the unknown vehicle population for this time period.

Spin leaves DC to transition business model from bikes to scooters

Spin reacted to the perceived popularity of scooters by completely pulling out of all markets in mid-August to retool its business model.

Due to a bug in the Spin data collection process, I only have data through mid-May, so I’m unable to properly analyze the time period immediately before Spin’s departure from DC. Based on the data I have, I can state that Spin averaged about 200 bikes on the street, while its pedal DoBi peers averaged at or above the the DDOT-mandated 400-vehicle cap.

Spin allowed its fleet size to dip below 150 bikes a few days after DDOT extended the dockless pilot to August. I can’t draw any definitive conclusions from so few data points, but I would not be surprised if this downward trajectory continued through the summer months.

Jump “jumps” to fill the void

When looking at the current dockless pilot landscape, it’s easy to conclude that that scooters have won out, but Jump’s ascension in the wake of its DoBi peers’ exit is also clear when looking at the data. Less than a week after Ofo and Mobike exit the DC market, Jump surpasses 200 bikes for the first time, increasing its fleet size from 126 to 414.

Similar to Lime scooters, Jump is having trouble maintaining a fleet of 400 vehicles, which is illustrated by the similarly sloped negatively lines for each operator starting in early August. This can been seen as a “good problem” for Jump; one explaination is that Jump is enjoying similar popularity as Lime scooters, necessitating additional fleet maintenance and downtime for charging.

]]>Fri, 05 Oct 2018 15:33:00 +0000Mark Sussman (Contributor)Five graphs that show how dockless bikeshare and CaBi work in DChttps://ggwash.org/view/68929/how-did-dockless-bikeshare-impact-cabi-heres-what-we-found
https://ggwash.org/view/68929/how-did-dockless-bikeshare-impact-cabi-heres-what-we-found

Over the past year, five dockless bikeshare companies have launched in DC. Four of them are either gone or are pivoting to scooters. But while they were here, how did their operations impact and compare with Capital Bikeshare's? I led a team at Georgetown University that investigated this question.

CaBi provides public data for the approximately 20 million trips taken to date, which served as the foundation of our analysis. For the dockless pilot trips, we collaborated with DDOT to obtain each dockless operator’s trip data from September 2017 through April 2018. The dockless trip data allowed us to see if our CaBi demand model was accurate and to answer some interesting questions DDOT posed about the pilot.

1 - Warmer temperature and US holidays have greatest impact on CaBi trips

The chart above shows the most important features from our model and whether each feature has a positive or negative impact on daily CaBi demand. For example, DC’s population, which has grown significantly over the eight years that CaBi has been in service, has a strong positive impact on CaBi demand. Conversely, people ride CaBi less if it rains hard.

The features that affect ridership most of all are weather-related. In particular, people ride much more often when it's warm but not humid. Interestingly, ridership declines most when there is a strong threat of rain, but actual rain is not as important.

We found that 80% of CaBi trips are taken by members as part of a daily commute. As a result, US holidays cause CaBi ridership to decrease significantly.

2 - Most dockless bicycles were replaced at an alarming rate

On average, dockless operators are replacing entire fleets more than three times in an eight-month span. The average bike lasted on the street for less than two months. I cannot say for certain why the lifespan of a dockless bike is so short without more operational data from the dockless operators.

In order to calculate the replacement rate, we simply counted all the bikes associated with a dockless operator’s trips in the data they provided. To illustrate this calculation, let’s say an operator placed 400 bikes on the street on average throughout the pilot period. If this operator used 2,600 total bikes during the pilot period, they would have replaced their entire fleet 6.5 times (2600/400 = 6.5).

Capital Bikeshare, on the other had, has only replaced 10% of its bike fleet over an eight-year period, and its bikes last close to 4.5 years on average. CaBi seems to have set the gold standard for bike maintenance for a bikeshare system.

3 - One-off ridership dominated the dockless pilot

A large part of our Capstone project was trying to understand dockless user behavior compared to the two CaBi user groups — members and casual users. It’s important to remember that each dockless operator is capped at 400 bikes during the pilot, while CaBi has more than 4,500 bikes.

Remembering the high dockless bicycle replacement rate, it comes at no surprise that the overwhelming majority of dockless users took five trips or fewer over the dockless pilot period. This statistic tells me that most people are not using dockless bikes for commuting purposes

Digging below the surface of this statistic offers a little more color. These results are a bit deceptive, since there was no way for us to determine how many total dockless trips a user took for all operators combined, only total per operator. It’s perfectly reasonable to assume that users tried out several dockless operators, myself included, which would not be captured here.

We saw two surges of new user activity. The first surge was in late October/early November when all five operators had largely put their full fleets on the streets for the first time. The second surge was in mid-March/early April when Lime introduced scooters. Lime did not differentiate between bikes and scooters, so we had to include scooter data in our analysis.

Given how new the concept of dockless bikes and scooters are to the US market, it’s not surprising that users just wanted to try out the service a few times out of curiosity.

4 - Our model expected more CaBi trips during the dockless pilot period

This figure shows the results of our machine learning model, which is considered to be highly predictive by data science standards.

Prior to the start of the dockless pilot, we were able to accurately predict CaBi ridership on a given day based on other data like weather, number of CaBi stations, and whether or not there was a Nationals game. During the pilot period, our model was less predictive — namely, the model expected more CaBi trips than actually occurred.

It’s possible that factors excluded from our model, like dockless trips, could be causing CaBi ridership to be lower than expected. This is what we were trying to prove with this analysis.

On average, the difference between expected and actual CaBi trips is approximately equal to 50% of the dockless trips taken on a given day, so it seems plausible that some of these dockless riders might have otherwise used CaBi. I believe it's likely that dockless bikes did indeed reduce CaBi's ridership somewhat during the dockless pilot period.

5 - 90% of dockless trips overlap with CaBi service area

The map on the left shows that 90% of dockless trips ended within a quarter mile of a CaBi station; the map on the right shows that the remaining 10% of dockless trips ended further away.

Another way we analyze dockless behavior was geographically. The maps above show the percent of dockless trips over the pilot period by Advisory Neighborhood Commission (ANC) district. The map on the left shows that 90% of dockless trips ended within a quarter mile of a CaBi station.

These trips center on ANC 2A, which encompasses Foggy Bottom and the western part of the National Mall — a further sign of casual dockless usage. A similar CaBi trip map (not shown here) centers on ANC 2C in the heart of downtown DC.

The map on the right shows that the remaining 10% of dockless trips ended further away from a CaBi station. The service area dramatically shifts to Wards 3, 4 and 5, but still has a large concentration in ANC 6D, which is home of attractions like the Tidal Basin and Nationals Park.

Noticeably absent from both maps are trips ending in Wards 7 or 8.

Lessons from our analysis

Based on this analysis, we learned that despite their obvious similarities, DoBi and CaBi are different services with different strengths and weaknesses. Dockless bikeshare may have eaten away a little at CaBi ridership, but not very much if any. That trade-off was some people using bikeshare in areas far from CaBi stations, though most dockless trips overlapped with CaBi service area. Also, DoBi's bicycle replacement rates were genuinely tremendous.